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/*=========================================================================
*
* Copyright NumFOCUS
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* https://www.apache.org/licenses/LICENSE-2.0.txt
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
*=========================================================================*/
// Software Guide : BeginCommandLineArgs
// INPUTS: {brainweb165a10f17.mha}
// ARGUMENTS: {WhiteMatterSegmentation.mhd}
// Software Guide : EndCommandLineArgs
#include "itkConfidenceConnectedImageFilter.h"
#include "itkCastImageFilter.h"
#include "itkCurvatureFlowImageFilter.h"
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
// Software Guide : BeginLatex
//
// This example is a 3D version of the previous ConfidenceConnected example.
// In this particular case, we are extracting the white matter from an input
// Brain MRI dataset.
//
// Software Guide : EndLatex
int
main(int argc, char * argv[])
{
if (argc < 3)
{
std::cerr << "Missing Parameters " << std::endl;
std::cerr << "Usage: " << argv[0];
std::cerr << " inputImage outputImage " << std::endl;
return EXIT_FAILURE;
}
using InternalPixelType = float;
constexpr unsigned int Dimension = 3;
using InternalImageType = itk::Image<InternalPixelType, Dimension>;
using OutputPixelType = unsigned char;
using OutputImageType = itk::Image<OutputPixelType, Dimension>;
using CastingFilterType =
itk::CastImageFilter<InternalImageType, OutputImageType>;
auto caster = CastingFilterType::New();
using ReaderType = itk::ImageFileReader<InternalImageType>;
using WriterType = itk::ImageFileWriter<OutputImageType>;
auto reader = ReaderType::New();
auto writer = WriterType::New();
reader->SetFileName(argv[1]);
writer->SetFileName(argv[2]);
using CurvatureFlowImageFilterType =
itk::CurvatureFlowImageFilter<InternalImageType, InternalImageType>;
auto smoothing = CurvatureFlowImageFilterType::New();
using ConnectedFilterType =
itk::ConfidenceConnectedImageFilter<InternalImageType, InternalImageType>;
auto confidenceConnected = ConnectedFilterType::New();
smoothing->SetInput(reader->GetOutput());
confidenceConnected->SetInput(smoothing->GetOutput());
caster->SetInput(confidenceConnected->GetOutput());
writer->SetInput(caster->GetOutput());
smoothing->SetNumberOfIterations(2);
smoothing->SetTimeStep(0.05);
confidenceConnected->SetMultiplier(2.5);
confidenceConnected->SetNumberOfIterations(5);
confidenceConnected->SetInitialNeighborhoodRadius(2);
confidenceConnected->SetReplaceValue(255);
InternalImageType::IndexType index1;
index1[0] = 118;
index1[1] = 133;
index1[2] = 92;
confidenceConnected->AddSeed(index1);
InternalImageType::IndexType index2;
index2[0] = 63;
index2[1] = 135;
index2[2] = 94;
confidenceConnected->AddSeed(index2);
InternalImageType::IndexType index3;
index3[0] = 63;
index3[1] = 157;
index3[2] = 90;
confidenceConnected->AddSeed(index3);
InternalImageType::IndexType index4;
index4[0] = 111;
index4[1] = 150;
index4[2] = 90;
confidenceConnected->AddSeed(index4);
InternalImageType::IndexType index5;
index5[0] = 111;
index5[1] = 50;
index5[2] = 88;
confidenceConnected->AddSeed(index5);
try
{
writer->Update();
}
catch (const itk::ExceptionObject & excep)
{
std::cerr << "Exception caught !" << std::endl;
std::cerr << excep << std::endl;
return EXIT_FAILURE;
}
return EXIT_SUCCESS;
}
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